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284 results for “Future climate”
Fig. 4 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus
Fig. 4 Area projected as suitable or unsutable under current and future (2081–2100) climatic conditions (km2) for the three tick species in comparison. a Ixodes ricinus. b Dermacentor reticulatus. c D. marginatus. The corresponding maps are shown in Figs. 1–3 in the main document. Future suitable conditions refers to the area (km2) projected as unsuitable under current climatic conditions but suitable under future climatic conditions (i.e., potential new range). Continuing suitable conditions refers to area (km2) projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable presence). Continuing unsuitable conditions refers to area (km 2) projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e. stable absence). Future unsuitable conditions refers to the area (km.2) projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction)
Fig. 2 Projected future changes for Dermacentor reticulatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus
Fig. 2 Projected future changes for Dermacentor reticulatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585. In dark blue: area projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction). In light blue: area projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e., stable absence). In orange: area projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable range). In red: area projected as unsuitable under current climatic conditions but suitable under future climatic conditions (i.e., potential new range). AUC = 0.8333 (average over 10 replicates using cross-validation, standard deviation = 0.001113603). Threshold to transform the logistic model output: 0.3816 (10% omission rate threshold). Maps were built using ESRI ArcGIS (Release 10.7, www.esri.com). Projection: Europe Albers Equal Area Conic
Fig. 2 Projected future changes for Dermacentor reticulatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus
Fig. 2 Projected future changes for Dermacentor reticulatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585. In dark blue: area projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction). In light blue: area projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e., stable absence). In orange: area projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable
Fig. 4 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus
Fig. 4 Area projected as suitable or unsutable under current and future (2081–2100) climatic conditions (km2) for the three tick species in comparison. a Ixodes ricinus. b Dermacentor reticulatus. c D. marginatus. The corresponding maps are shown in Figs. 1–3 in the main document. Future suitable conditions refers to the area (km2) projected as unsuitable under current climatic conditions but suitable under future climatic conditions (i.e., potential new range). Continuing suitable conditions refers to area (km2) projected as suitable under
Fig. 1 Projected future changes for Ixodes ricinus until 2081–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus
Fig. 1 Projected future changes for Ixodes ricinus until 2081–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585. In dark blue: area projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction). In light blue: area projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e., stable absence). In orange: area projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable range). In red:
Fig. 5 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus
Fig. 5 Potential co-occurrence under current and future climatic conditions. a Under near current climatic conditions (1970–2000). b Under projected future climatic conditions (exemplarily for SSP 245) for the period 2041–2060. c Under projected future climatic conditions (SSP 245) for the period 2081–2100. Colors indicate areas where climatic suitability is projected for the respective species; for non-mentioned species ("none of them"), the area is climatically unsuitable according to the modelling results. The thresholds to transform the logistic model output (10% omission rate threshold) are as follows: 0.3368 for Ixodes ricinus, 0.3816 for Dermacentor reticulatus, and 0.4298 for D. marginatus. Maps were built using ESRI Arc-GIS (Release 10.7, www.esri.com). Projection: Europe Albers Equal Area Conic. (A hatch-based version of this figure is additionally provided in the Supplementary Material: Figure S11.)
Fig. 3 Projected future changes for Dermacentor marginatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus
Fig. 3 Projected future changes for Dermacentor marginatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585. In dark blue: area projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction). In light blue: area projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e., stable absence). In orange: area projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable range). In red: area projected as unsuitable under current climatic conditions but suitable under future climatic conditions (i.e., potential new range). AUC = 0.8229 (average over 10 replicates using cross-validation, standard deviation = 0.001121953). Threshold to transform the logistic model output: 0.4298 (10% omission rate threshold). Maps were built using ESRI ArcGIS (Release 10.7, www.esri.com). Projection: Europe Albers Equal Area Conic
Data from: Hydrology induces intraspecific variation in freshwater fish morphology under contemporary and future climate scenarios
<p>Datasets for manuscript "Andres, K. J., Chien, H., and Knouft, J. H. Hydrology induces intraspecific variation in freshwater fish morphology under contemporary and future climate scenarios. Science of the Total Environment. <a href="https://doi.org/10.1016/j.scitotenv.2019.03.292">https://doi.org/10.1016/j.scitotenv.2019.03.292</a>"</p> <p>landmarks.zip: landmarks digitized on images of 1081 specimens using TpsDig2 software.</p> <p>streamflow_estimates.csv: Contemporary (1980-2009) and future (2070-2099) streamflow estimates [avg: average annual streamflow discharge (m3 s-1); cv: coefficient of variation of annual discharge] in sub-basins containing populations of 6 minnow species in IL, USA</p>
Figure 4 in The potential effects of future climate change on suitable habitat for the Taiwan partridge (Arborophila crudigularis): an ensemble-based forecasting method
Figure 4. Mean suitability for Arborophila crudigularis under baseline climate conditions and future climate scenarios. cccma and csiro represent two general circulation models; RCP2.6, and RCP8.5 represent two greenhouse gas emission scenarios; EN is entire suitable habitat; PR is presence records.
Figure 2 in The potential effects of future climate change on suitable habitat for the Taiwan partridge (Arborophila crudigularis): an ensemble-based forecasting method
Figure 2. Performance of each model for predicting the suitable habitat for Arborophila crudigularis. GLM: Generalized linear model; GBM: generalized boosting model; GAM: generalized additive model; CTA: classification tree analysis; ANN: artificial neural network; FDA: flexible discriminant analysis; MARS: multiple adaptive regression splines; RF: random forest; MAXENT: maximum entropy model.
Figure 6 in The potential effects of future climate change on suitable habitat for the Taiwan partridge (Arborophila crudigularis): an ensemble-based forecasting method
Figure 6. Changes in suitable habitat for Arborophila crudigularis under the RCP8.5 emission scenario. cccma and csiro represent two general circulation models.
Figure 5 in The potential effects of future climate change on suitable habitat for the Taiwan partridge (Arborophila crudigularis): an ensemble-based forecasting method
Figure 5. Changes in suitable habitat for Arborophila crudigularis under the RCP2.6 emission scenario. cccma and csiro represent two general circulation models.
Estimating future climate change impacts on human mortality and crop yields via air pollution: supplemental files
<p>Atmospheric chemistry model output and other gridded data sets necessary to estimate human mortality and crop yield losses associated with future climate change, as used in Murray et al. [PNAS, 2024] doi:10.1073/pnas.2400117121.</p>
Baseline and Future (2050s and 2090s) Climate Suitability Scores for 116 Useful Tree Species and 220 locations from Côte d'Ivoire, Ghana and Guinea
<p>Climate suitability scores were calculated for 116 Useful Tree Species identified by filtering Top830+ native tree species from Côte d'Ivoire, Ghana and Guinea via the <a href="https://patspo.shinyapps.io/GlobalUsefulTrees/">GlobalUsefulNativeTrees</a> database and checking for the availability of globally observed environmental ranges from the <a href="https://doi.org/10.5281/zenodo.13132613">TreeGOER</a> database.</p> <ul> <li>Score = 3 means that in 'environmental space' the planting site occurs within the 25% - 75% species's range (as documented in the <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.16914" target="_blank" rel="noopener">TreeGOER</a> ) for all variables</li> <li>Score = 2 corresponds to the 5% - 95% species's range for all variables. For some variables, the planting site occurs outside the 25% - 75% species's range.</li> <li>Score = 1 corresponds to the 0% - 100% species's range for all variables. For some variables, the planting site occurs outside the 5% - 95% species's range.</li> <li>Score = 0 means that the planting site occurs outside the 0% - 100% species's range for some of the variables</li> <li>Score = -1 means that the species is not documented by TreeGOER</li> </ul> <p>Locations corresponded to cities and weather stations from the three target countries sourced from the <a href="https://doi.org/10.5281/zenodo.10004594">CitiesGOER</a> and <a href="https://doi.org/10.5281/zenodo.12679832">ClimateForecasts</a> databases, respectively. Both these databases provide bioclimatic conditions for the historical (baseline) and three future climate change scenarios. Bioclimatic variables for future climates correspond to the median values from 24 Global Climate Models (GCMs) for Shared Socio-Economic Pathway (SSP) 1-2.6 for the 2050s (2041-2060), from 21 GCMs for SSP 3-7.0 for the 2050s and from 13 GCMs for SSP 5-8.5 for the 2090s.</p> <p>Investigations were made for two different sets of bioclimatic variables, allowing for sensitivity analysis:</p> <ul> <li>One set of bioclimatic variables included BIO01 (mean annual temperature), BIO12 (total annual precipitation), climaticMoistureIndex, monthCountByTemp10 (number of months with average temperature above 10 degrees), growingDegDays5, BIO05 (maximum temperature of the warmest month), BIO06 (minimum temperature of teh coldest month), BIO16 (precipitation of the wettest quarter), BIO17 (precipitation of the driest quarter) and MCWD (Maximum Climatological Water Deficit). These are the same bioclimatic variables available internally in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> for climate filtering.</li> <li>One set only included BIO01 (mean annual temperature), which is the single bioclimatic variables available for the BGCI <a href="https://cat.bgci.org/">Climate Assessment Tool</a>.</li> </ul> <p>Calculations were made with similar scripting pipelines in the <em>R</em> statistical environment as documented here: <a href="https://rpubs.com/Roeland-KINDT/1168650">https://rpubs.com/Roeland-KINDT/1168650</a>. These scripts use similar calculations methods as those used for the global case studies of the TreeGOER manuscript (Kindt <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">2023</a>), and used internally in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> online database. Interested readers should especially refer to the manuscript for further details on methods used and their justification.</p> <p>The maps show the frequency distribution of tree species with climate scores 3, 2, 1 and 0, excluding 18 species not documented by the TreeGOER.</p> <p> </p> <p><strong>References</strong></p> <ul> <li>Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>.</li> <li>Kindt, R. (2024). TreeGOER: Tree Globally Observed Environmental Ranges (2024.07) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.13132613" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13132613</a></li> <li>Kindt, R., Graudal, L., Lillesø, JP.B. <em>et al.</em> (2023). GlobalUsefulNativeTrees, a database documenting 14,014 tree species, supports synergies between biodiversity recovery and local livelihoods in landscape restoration. <em>Sci Rep</em> <strong>13</strong>, 12640. <a href="https://doi.org/10.1038/s41598-023-39552-1">https://doi.org/10.1038/s41598-023-39552-1</a></li> <li>Kindt, R. (2023). CitiesGOER: Globally Observed Environmental Data for 52,602 Cities with a Population ≥ 5000 (2023.10) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.10004594" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10004594</a></li> <li>Kindt, R. (2024). ClimateForecasts: Globally Observed Environmental Data for 15,504 Weather Station Locations (2024.07) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.12679832" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12679832</a></li> <li>Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. <em>International Journal of Climatology</em>, <em>37</em>(12), 4302–4315. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a></li> <li>Title, P. O., & Bemmels, J. B. (2018). ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. <em>Ecography</em>, <em>41</em>(2), 291–307. <a href="https://doi.org/10.1111/ecog.02880">https://doi.org/10.1111/ecog.02880</a></li> <li>Opendatasoft (2023) Geonames - All Cities with a population > 1000. <a href="https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name">https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name</a> (accessed 22-JULY-2023)</li> <li>Meteostat (2024) Weather stations: Lite dump with active weather stations. <a href="https://github.com/meteostat/weather-stations">https://github.com/meteostat/weather-stations</a> (accessed 17-FEB-2024)</li> </ul> <p> </p> <p><strong>Funding</strong></p> <p>The data sets and maps available in this archive were created within the context of an agreement between The International Centre for Research in Agroforestry (ICRAF) and WORLD UNIVERSITY SERVICE OF CANADA (WUSC) for a <em><a href="https://ceci.org/en/projects/nature-based-climate-adaptation-guinean-forest-west-africa-sbn-guinean-forests">Nature-based climate adaptation project in the Guinean forests of West Africa (NbS Guinean Forests)</a></em> funded by <a href="https://www.international.gc.ca/global-affairs-affaires-mondiales/home-accueil.aspx?lang=eng">Global Affairs Canada</a>.</p>
Moana Ocean Future Climate (Sample Daily Fields)
<p>The <strong>Moana Ocean Future Climate </strong>downscaling is a set of dynamical downscalings for New Zealand waters combining boundary and surface forcing from the New Zealand Earth System Model with the Moana Ocean Hindcast ROMS configuration. MOFC has been run for a 20 year reference period (1990-2010) and three future climate experiments (SSP3-70 2030-2060, SSP3-70 2070-2099 and SSP2-45 2070-2099). While this repository contains sample files, the full dataset can be downloaded from the project webpage at https://www.moanaproject.org/</p> <p><br>Model results are available as hourly and daily average values for temperature, salinity, velocity (u, v, w), and sea surface elevation.</p> <p> </p> <p>Full descriptions of these datasets are provided in Roach et al. (submitted to GMD).</p>
Oriental Honey-Buzzards Dataset | Climate change leads to range contraction for the Oriental Honey-Buzzards: How to point out the future conservation strategies?
<p>This dataset contains raster data (.TIF) in probability and binary outputs of oriental honey-buzzards distribution within the wintering and breeding areas under changing climate.</p> <p><strong>File Size</strong>: ~184 MB (13.8 MB in compressed ZIP file)</p> <p><strong>Format File</strong>:</p> <p><em>ohb_A_B_C</em>.tif (.tfw; .XML; .dbf)</p> <p><strong>A</strong>: breeding or wintering</p> <p><strong>B</strong>: timepoint and scenario. e.g., 2050ssp5 or 2010ssp2</p> <p><strong>C</strong>: binary or probability outputs. e.g., bin or prob. <em>Note: for binary maps, value 0: non-suitable areas for OHB and value 1: suitable areas for OHB</em></p> <p>For further inquiries. Please contact: aryo_acondro@apps.ipb.ac.id</p>
Supplementary data: "Future operation of hydropower in Europe under high renewable penetration and climate change"
<p>This dataset contains modelled inflow time series described in the paper "Future operation of hydropower in Europe<br> under high renewable penetration and climate change".</p> <p>The inflow is derived from ten different combinations of five General Circulation Models and two Regional Climate Models at the beginning of the century ("Hydro_inflow_BOC_(...).csv"), 1991 - 2020, and at the end of the century ("Hydro_inflow_EOC_(...).csv"), 2071 - 2100, under three CO2-emissions scenarios (RCP2.6, RCP4.5, and RCP8.5). The ensemble mean for each emissions scenario is presented as well.</p> <p>The historical data that is not confidential is given as well. See "data_sources" for sources.</p>
Conservation of woody species in China under future climate and land-cover changes
<ol> <li>Climate and land-cover changes are major threats to biodiversity, and their impacts are expected to intensify in the future. Protected areas (PAs) are crucial for biodiversity conservation. However, their effectiveness under future climate and land-cover changes remains to be evaluated. Moreover, the impacts of climate and land-cover changes on multi-dimensions of biodiversity are rarely considered when expanding PAs.</li> <li>Using distributions of 8732 woody species in China and species distribution models, we identified species that will be threatened by future climate and land-cover changes (i.e. species with significant projected loss of suitable habitats by the 2070s) under different dispersal scenarios. We then estimated the geographical patterns in species richness (SR) and phylogenetic diversity (PD) of these threatened species, evaluated the effectiveness (i.e. the changes in SR and PD) of Chinese PAs, and identified conservation priorities for future PA expansion.</li> <li>Approximately 12-38% of woody species will be threatened under different scenarios. These species tend to be clustered in the tree of life, and their SR and PD show consistent spatial patterns, being highest at low latitudes. PAs currently protect 90% of these threatened species. However, their SR and PD of threatened species within PAs will decrease by 30-40% by the 2070s, which reduces the PA effectiveness, especially for PAs at low elevations and those with low topographic heterogeneity and high natural vegetation loss.</li> <li>The conservation priorities identified from the SR and PD of the threatened species are mainly in mountains in southern China, the Yunnan-Guizhou Plateau, and Taiwan Island. PA expansion and ecological corridors in these regions are needed to conserve these threatened species.</li> <li> <i>Synthesis and applications.</i> We present a systematic study of the impacts of future climate and land-cover changes on the conservation status of woody species and PA effectiveness in China. Our results suggest that future climate and land-cover changes will reduce PA effectiveness, and the spatial prioritization of biodiversity conservation should consider the influences of future global changes on biodiversity. These results shed new light on the conservation priorities for the post-2020 expansion of PAs in China.</li> </ol>
Global diversity patterns of larger benthic foraminifera under future climate change
<p><span>Global warming threatens the viability of tropical coral reefs and associated marine calcifiers, including symbiont-bearing larger benthic foraminifera (LBF). The impacts of current climate change on LBF are debated because they were particularly diverse and abundant during past warm periods. Studies on the responses of selected LBF species to changing environmental conditions reveal varying results. </span><span>Based on a comprehensive review of the scientific literature on LBF species occurrences, we applied species distribution modeling using Maxent to estimate present-day and future species richness patterns on a global scale for the time periods 2040–2050 and 2090–2100. </span><span> </span><span>For our future projections, we focus on Representative Concentration Pathway 6.0 from the Intergovernmental Panel on Climate Change, which projects mean surface temperature changes of +2.2°C by the year 2100. This data set comprises all raw data and results. </span>Our results suggest that species richness in the Central Indo-Pacific is two to three times higher than in the Bahamian ecoregion, which we have identified as the present-day center of LBF diversity in the Atlantic. Our future predictions project a dramatic temperature-driven decline in low-latitude species richness and an increasing widening bimodal latitudinal pattern of species diversity. While the central Indo-Pacific, now the stronghold of LBF diversity, is expected to be most pushed outside of the currently realized niches of most species, refugia may be largely preserved in the Atlantic. LBF species will face large-scale non-analogous climatic conditions compared to currently realized climate space in the near future, as reflected in the extensive areas of extrapolation, particularly in the Indo-Pacific. Our study supports hypotheses that species richness and biogeographical patterns of LBF will fundamentally change under future climate conditions, possibly initiating a faunal turnover by the late 21st century.</p>
Vulnerability of estuarine systems in the contiguous United States to water quality change under future climate and land-use
<p>Changes in climate and land-use and land-cover (LULC) are expected to influence surface water runoff and nutrient characteristics of estuarine watersheds, but the extent to which estuaries are vulnerable to altered nutrient loading under future conditions is poorly understood. The present work aims to address this gap through the development of a new vulnerability assessment framework that accounts for (1) estuarine exposure to projected changes in total nitrogen (TN) and total phosphorus (TP) loads as a function of LULC and climate change under several scenarios to altered nutrient loads, (2) sensitivity (i.e., how responsive estuaries are to altered nutrient loads), and (3) adaptive capacity (i.e., how the socio-ecological system can use existing resources to reduce the impacts associated with increased exposure). The framework was applied to 112 estuaries and their contributing watersheds across the contiguous U.S., specifically to look at regional variability in estuarine vulnerability to nutrient loading. Study findings revealed that the largest increases in estuarine nutrient loads are expected in the North and South Atlantic regions and eastern Gulf of Mexico, while the lowest increase is expected in the North and South Pacific regions and the western Gulf of Mexico. However, the North Atlantic and the South Pacific had the highest adaptive capacity, which could potentially counteract the effects of LULC and climate change on nutrient loads. Our findings illustrate the benefits of integrating natural and socio-ecological factors to identify opportunities to develop adaptation plans and policies to mitigate ecological degradation in vitally important estuaries. A<a href="https://lisemontefiore.shinyapps.io/estuarine_vulnerability/"> web-based application</a> has been developed to visualize and download the data.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.